CN116631488B - Storage performance detection method and system for flash memory - Google Patents
Storage performance detection method and system for flash memory Download PDFInfo
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Abstract
The invention discloses a method and a system for detecting storage performance of a flash memory, which relate to the technical field of data processing, and the method comprises the following steps: acquiring a storage performance detection index and a storage performance index threshold; acquiring a detection index state value matrix; extracting performance detection indexes which do not meet the threshold value of the storage performance index, and setting the performance detection indexes as performance indexes to be optimized; performing association analysis according to the performance index to be optimized to obtain the processing parameters to be optimized of the memory; optimally designing the processing parameters to be optimized of the memory according to the performance index to be optimized; adding the performance index to be optimized and the memory optimization scheme into a memory performance detection result; and sending the data to the floating gate type memory production management terminal. The invention solves the technical problems of single storage performance detection function, long detection period and low intelligent degree of the flash memory in the prior art, achieves the technical effects of improving the detection efficiency and the detection accuracy, and provides an intelligent scheme for optimizing the production of the memory.
Description
The invention relates to the technical field of data processing, in particular to a method and a system for detecting storage performance of a flash memory.
Background
With the rapid development of big data technology, the generated mass data needs to be stored timely and reliably, so that the utilization degree of the data can be improved to the greatest extent. Flash memory is a nonvolatile memory that does not lose data in the memory even when power is turned off, and therefore, flash memory is widely used for data storage.
At present, the storage performance of the flash memory is mainly tested in multiple aspects by professional testers, and the test results are summarized and analyzed to determine the reliability of the flash memory for storing data. However, in the actual testing process, when a tester performs data analysis on a large amount of test data, the data analysis result is unreliable due to factors such as working capacity of the tester and insufficient human resources, and the feedback period is too long, so that the performance detection result cannot be fed back in time. Moreover, only a single detection result is obtained, and the reasons for the occurrence of the detection result are not traced, so that the subsequent analysis procedures are increased. The prior art has the technical problems of single storage performance detection function, long detection period and low intelligent degree of the flash memory.
Disclosure of Invention
The application provides a method and a system for detecting storage performance of a flash memory, which are used for solving the technical problems of single storage performance detection function, long detection period and low intelligent degree of the flash memory in the prior art.
In view of the above, the present application provides a method and a system for detecting storage performance of a flash memory.
In a first aspect of the present application, there is provided a method for detecting storage performance of a flash memory, wherein the method is applied to a floating gate type memory detection module, and includes:
acquiring a storage performance detection index and a storage performance index threshold;
detecting the flash memory by traversing the storage performance detection indexes to obtain a detection index state value matrix;
extracting performance detection indexes of which the detection index state value matrix does not meet the storage performance index threshold value, and setting the performance detection indexes as performance indexes to be optimized;
performing association analysis according to the performance index to be optimized to obtain processing parameters to be optimized of a memory;
optimizing design is carried out on the processing parameters to be optimized of the memory according to the performance indexes to be optimized, and a memory optimization scheme is obtained;
adding the performance index to be optimized and the memory optimization scheme into a memory performance detection result;
And sending the storage performance detection result to a floating gate type memory production management terminal.
In a second aspect of the present application, there is provided a storage performance detection system of a flash memory, the system comprising:
the detection index obtaining module is used for obtaining a storage performance detection index and a storage performance index threshold;
the state value matrix obtaining module is used for traversing the storage performance detection indexes to detect the flash memory and obtaining a detection index state value matrix;
the performance optimization setting module is used for extracting performance detection indexes of which the detection index state value matrix does not meet the storage performance index threshold value and setting the performance detection indexes as performance indexes to be optimized;
the processing parameter obtaining module is used for carrying out association analysis according to the performance index to be optimized to obtain the processing parameter to be optimized of the memory;
the optimization scheme obtaining module is used for carrying out optimization design on the processing parameters to be optimized of the memory according to the performance indexes to be optimized to obtain a memory optimization scheme;
The detection result obtaining module is used for adding the performance index to be optimized and the memory optimization scheme into a storage performance detection result;
and the result sending module is used for sending the storage performance detection result to the floating gate type memory production management terminal.
One or more technical schemes provided by the application have at least the following technical effects or advantages:
the method comprises the steps of obtaining storage performance detection indexes and storage performance index thresholds of a flash memory, detecting the flash memory according to each detection index in the storage performance detection indexes, obtaining a detection index state value matrix according to detection results, extracting performance detection indexes of which the detection index state value matrix does not meet the storage performance index thresholds, setting an extraction result as performance indexes to be optimized, carrying out association analysis according to the performance indexes to be optimized, obtaining processing parameters to be optimized of the memory, carrying out optimization design on the processing parameters to be optimized of the memory by utilizing the performance indexes to be optimized, obtaining a memory optimization scheme, adding the performance indexes to be optimized and the memory optimization scheme into the storage performance detection results, and sending the storage performance detection results to a floating gate type memory production management terminal. The intelligent detection of the storage performance of the flash memory is achieved, the detection period is shortened, the production of the memory is optimally managed according to the toilet cleaning result, and the technical effect of improving the quality of the flash memory is further achieved.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for detecting storage performance of a flash memory according to an embodiment of the present application;
FIG. 2 is a schematic flow chart of setting a performance index to be optimized in a method for detecting storage performance of a flash memory according to an embodiment of the present application;
FIG. 3 is a schematic flow chart of obtaining processing parameters to be optimized of a flash memory in a method for detecting storage performance of the flash memory according to an embodiment of the present application;
fig. 4 is a schematic diagram of a storage performance detection system of a flash memory according to an embodiment of the present application.
Reference numerals illustrate: the system comprises a detection index obtaining module 11, a state value matrix obtaining module 12, a performance optimization index setting module 13, a processing parameter obtaining module 14, an optimization scheme obtaining module 15, a detection result obtaining module 16 and a result sending module 17.
Description of the embodiments
The application provides a method and a system for detecting the storage performance of a flash memory, which are used for solving the technical problems of single storage performance detection function, long detection period and low intelligent degree of the flash memory in the prior art.
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. It will be apparent that the described embodiments are only some, but not all, embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
It should be noted that the terms "comprises" and "comprising," along with any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or server that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed or inherent to such process, method, article, or apparatus, but may include other steps or modules not expressly listed or inherent to such process, method, article, or apparatus.
Examples
As shown in fig. 1, the present application provides a method for detecting storage performance of a flash memory, wherein the method is applied to a floating gate type memory detection module, and includes:
step S100: acquiring a storage performance detection index and a storage performance index threshold;
further, the step S100 of the embodiment of the present application further includes:
step S110: the storage performance detection indicators comprise an electrical storage performance detection indicator and an optoelectronic storage performance detection indicator, wherein the electrical storage performance detection indicator comprises one or more of a current-voltage curve, an electrical storage transfer characteristic curve, a charge retention performance and a service life, and the optoelectronic storage performance detection indicator comprises one or more of an electrical storage transfer characteristic curve, FN tunneling mechanism performance, light response performance, light auxiliary erasing performance and optoelectronic multi-value storage performance;
step S120: the storage performance index threshold is set by traversing one or more of the current-voltage curve, the electrical storage transfer characteristic, the charge retention performance, and the service life, and one or more of the electrical storage transfer characteristic, the FN tunneling mechanism performance, the photoresponsive performance, the optically assisted erasure performance, and the optoelectronic multi-valued storage performance.
In particular, as two-dimensional materials in which electrons can perform planar motion only on the nano-scale of two dimensions are increasingly used, memories having smaller dimensions and lower power consumption are increasingly used. The floating gate type memory has the advantages of simpler integrated process and longer charge holding time, and is widely used in nonvolatile memories. The method for detecting the storage performance of the flash memory is applied to the floating gate type memory detection module, and the floating gate type memory detection module is used for detecting the storage performance of the floating gate type memory. The floating gate type memory detection module is a functional module for detecting multiple storage performances of the flash memory, and preferably, a detection instrument in communication connection with the detection module is provided with a low-temperature probe station, a semiconductor analyzer and a laser light source. And the SMU interface which is used for analyzing the semiconductor is connected with the conductive probe through a wire and an adapter, and the semiconductor analyzer is in communication connection with the floating gate type memory detection module through a communication interface, so that the test data processed and output by the semiconductor analyzer are input into the floating gate type memory detection module. Providing the basis analysis data for analyzing the performance of the memory.
Specifically, the storage performance detection index is a unit or a method for measuring the storage performance of the floating gate type memory, and comprises an electrical storage performance detection index and an optoelectronic storage performance detection index. The electrical storage performance is a characteristic of the ability to perform writing and erasing of data by voltage driving intrinsic to the floating gate type memory device. The electrical storage performance detection indicator is a method for measuring the capacity of the electrical storage performance of the floating gate type memory and comprises one or more of a current-voltage curve, an electrical storage transfer characteristic curve, a charge retention performance and a service life. That is, when the electrical storage performance of the floating gate type memory is measured using the electrical storage performance detection index, the electrical storage performance of the floating gate type memory may be detected using only one of the electrical storage performance detection indexes, for example, using a current-voltage curve. Performance detection may also be performed using a plurality of the electrically stored performance indicators, wherein the number of indicators in the plurality of indicators is 1 or more. The electrical storage performance of the floating gate type memory is illustratively tested using the electrical storage transfer characteristic and the charge retention performance.
Specifically, the current-voltage curve is a curve for visually reflecting the change condition of the current and the voltage of the memory, and provides a basis for analyzing the conductivity and the contact resistance of the material in the memory. The electrical storage transfer characteristic curve is a curve reflecting the degree to which the source-drain current varies with the variation of the floating gate voltage. The charge retention performance is the length of time that charge is retained in the floating gate. The service life is the service life of the floating gate memory.
Specifically, the photoelectric storage performance is the capability characteristic of the floating gate type memory for storing optical signals and electric signals and releasing photon signals. The photoelectric storage performance detection index is a method for measuring the capacity of the photoelectric storage performance of the floating gate type memory and comprises one or more of an electric storage transfer characteristic curve, FN tunneling mechanism performance, light response performance, light auxiliary erasing performance and photoelectric multi-value storage performance. In other words, the photoelectric storage performance detection index may be used alone or a plurality of indexes may be used simultaneously. Wherein the electrical storage transfer characteristic is a transfer characteristic in different gate voltage ranges after the laser is irradiated on the surface of the memory. The FN tunneling mechanism performance is that charges are formed in graphene and PtS 2 Capacity for tunneling between. The light response property is the degree to which a material is sensitive to light. The light assisted erase performance is the ability of light to reduce the erase voltage of the device by facilitating tunneling. The photoelectric multi-value storage performance is the voltage and optical power range in which the memory can perform multi-value storage.
Specifically, the storage performance index threshold is set by an index used in the detection process according to one or more of the current-voltage curve, the electrical storage transfer characteristic curve, the charge retention performance, and the service life, and one or more of the electrical storage transfer characteristic curve, the FN tunneling mechanism performance, the photoresponsive performance, the photo-assisted erasure performance, and the photoelectric multi-value storage performance. The storage performance index threshold is a numerical range obtained after setting the lowest performance index value corresponding to each storage performance index when the storage meets the performance requirement, and is set by a worker by himself, and the method is not limited.
Step S200: detecting the flash memory by traversing the storage performance detection indexes to obtain a detection index state value matrix;
specifically, the performance of the flash memory is detected by using the storage performance detection index, and the number of elements in the detection index state value matrix is determined according to the number of the used detection indexes, wherein each element in the matrix corresponds to a state value of the storage performance detection index. The flash memory is a memory for performing performance detection and is a floating gate type memory. The monitoring index state value matrix reflects state values corresponding to all indexes when the flash memory is detected.
Specifically, the state value of the current-voltage curve index is that a flash memory is arranged in a cavity of a probe station, tinfoil paper is used for shading light, the device is arranged in a dark state environment, and PtS is carried out 2 The electrodes on both sides are connected with the probe, then the drain voltage is set to be 0V, the source voltage is gradually increased from-10 mV to 10mV, the increasing step length is 0.1mV, and then the semiconductor analyzer is operated to obtain the semiconductor analyzer. The index state value corresponding to the electrical storage transfer characteristic curve is obtained by scratching silicon oxide at the edge of the memory, connecting the silicon oxide with a gate probe, and further connecting PtS 2 The two ends are respectively connected with a source electrode probe and a drain electrode probe, then the source electrode bias voltage is set to be constant and respectively 10mV and 0V, the grid voltage is used as a variable, and the test range is from10V increases in sequence to->30V, and then the measured result was set as an electrical storage transfer characteristic curve.
Specifically, the index state value corresponding to the charge retention performance is obtained by erasing and writing-20V by applying +20v voltage written to the gate, opening the I-T test module after tunneling of charge is completed, removing the gate voltage, setting a bias voltage of 10mV, measuring a change curve of source-drain current with time, and obtaining the charge retention time according to the curve. The index state value corresponding to the service life is obtained by setting 1000 periodic erasing voltages, observing a periodic variation curve of current along with time, and extracting and reading the high-low resistance state current to obtain a relatively stable switching ratio, so that the device has a longer cycle life.
Specifically, the index state value corresponding to the electrical storage transfer characteristic curve is obtained by irradiating 532nm laser on the surface of a sample, measuring the transfer characteristic curve by the same method as in the dark state, and comparing the difference between the dark state and the curve under illumination. The index state value corresponding to the FN tunneling mechanism performance is obtained by constructing a graphene/h-BN/PtS 2 tunneling junction device by using a mechanical stripping and fixed-point transfer method, and measuring the graphene and PtS 2 And (5) obtaining the tunneling current after analysis. The index state value corresponding to the light response performance is obtained by applying grid voltage of +20V in a dark state, enabling the device to reach a high resistance state, measuring a current-time curve under 10mV bias voltage, opening a 532nm laser after a certain time interval, and observing a current change curve of the device along with time. The index state value corresponding to the optical auxiliary erasing performance is obtained by firstly placing the device in a dark state environment, writing an erasing program to read the change condition of the switching ratio along with the grid voltage amplitude, then setting an optical pulse with the same phase as the electric pulse, and analyzing the influence degree of the voltage amplitude and the optical power on the switching ratio of the device respectively. The index state value corresponding to the photoelectric multi-value storage performance is that the memory is adjusted to a high resistance state by utilizing a grid voltage of +30V, 532nm illumination pulse is applied to the memory, each conductivity state of the memory is read in a retentive mode, and the influence of the number of the light pulse and the light power on the multi-value storage is determined according to the read data.
Step S300: extracting performance detection indexes of which the detection index state value matrix does not meet the storage performance index threshold value, and setting the performance detection indexes as performance indexes to be optimized;
further, as shown in fig. 2, extracting the performance detection index whose detection index state value matrix does not meet the storage performance index threshold, and setting the performance detection index as the performance index to be optimized, step S300 of the embodiment of the present application further includes:
step S310: traversing the storage performance index threshold value, and setting a fault tolerance deviation interval;
step S320: constructing an index state detection matrix according to the storage performance index threshold value and the fault tolerance deviation interval;
step S330: and setting elements which do not meet the index state detection matrix in the detection index state value matrix as the performance indexes to be optimized.
Specifically, the detection index state value is screened by using the storage performance index threshold value, and the performance index incapable of meeting the index threshold value in the matrix is extracted and set as the performance index to be optimized. The performance index to be optimized is an index which cannot meet the requirement after performance detection is performed on the flash memory, and numerical values corresponding to the index are required to be optimized, so that the performance of the flash memory can meet the requirement.
Specifically, the size of the fault-tolerant deviation interval is set according to the range size of the storage performance index threshold. And setting a numerical deviation value of each index in the storage performance index threshold within a reasonable range, and setting the numerical deviation value as the fault tolerance deviation interval. The fault-tolerant deviation interval is an unavoidable deviation range which is considered to occur due to the accuracy of the detection instrument and the operation mode of the detection personnel.
Specifically, the element value range in the index state detection matrix is constructed according to the storage performance index threshold value and the fault tolerance deviation interval. And comparing the detection index state value matrix with the index state detection matrix to obtain a state value which cannot meet the corresponding range of the element values in the matrix, and setting the performance index corresponding to the state value as the performance index to be optimized. Therefore, the technical effects of screening and dimension reduction on the performance index of the memory and improving the accuracy and processing efficiency of the detection result of the performance of the memory are achieved.
Step S400: performing association analysis according to the performance index to be optimized to obtain processing parameters to be optimized of a memory;
Further, as shown in fig. 3, the correlation analysis is performed according to the performance index to be optimized to obtain the processing parameter to be optimized of the memory, and step S400 of the embodiment of the present application further includes:
step S410: performing primary association analysis according to the performance index to be optimized to obtain a memory element to be optimized;
step S420: acquiring processing parameters of the element to be optimized according to the element to be optimized of the memory;
step S430: and carrying out secondary association analysis on the processing parameters of the element to be optimized according to the performance index to be optimized, and obtaining the processing parameters to be optimized of the memory.
Specifically, the first-level association analysis is performed according to the performance index to be optimized, by using a memory element as an independent variable and the performance index to be optimized as a dependent variable, and by using the association analysis function, the association between the performance index and the memory element is analyzed. When the first-level association degree analysis result is greater than or equal to the first-level association degree threshold value, the corresponding memory element is set as the memory element to be optimized.
Specifically, the element to be optimized of the memory is an element which is related to an index that the memory cannot meet performance requirements among elements forming the flash memory, and the processing quality of the element cannot meet the requirements, so that the performance of the element cannot meet the requirements. The processing parameters of the element to be optimized are parameters of various processes in the production and processing process of the element to be optimized, and the parameters comprise the processing parameters of element length, element width, welding parameters, ball milling speed and the like. And further, carrying out secondary association analysis on the association degree corresponding to the processing parameters of the element to be optimized by using the performance index to be optimized, so as to carry out dimension reduction analysis on the processing parameters and obtain the processing parameters to be optimized of the memory. The processing parameters to be optimized of the memory are parameters which need to be subjected to parameter adjustment so that the performance of the element of the memory meets the requirements. And performing secondary association analysis by taking the processing parameters of the element to be optimized as independent variables and the performance index to be optimized as dependent variables, and determining the processing parameters with higher association as the processing parameters to be optimized of the memory.
Further, performing a secondary association analysis on the processing parameters of the element to be optimized according to the performance index to be optimized to obtain the processing parameters to be optimized of the memory, and step S430 of the embodiment of the present application further includes:
step S431: taking any one of the processing parameters of the element to be optimized as an independent variable, taking the performance index to be optimized as a dependent variable, and collecting processing record data of a memory;
step S432: acquiring a relevance analysis function:
wherein,characterizing a first association coefficient, ">Characterizing a second association coefficient, ">Characterizing the association degree->Characterizing the processing parameters of any element to be optimized, < ->Characterizing performance index to be optimized, < >>Characterizing the i-th pair of memory process record data>Variable amount of->Characterizing the i-th pair of memory process record data>Variable amount of->Characterizing the total logarithm of memory processing record data;
step S433: performing relevance evaluation based on the relevance analysis function according to the memory processing record data to obtain a secondary relevance analysis result;
step S434: and when the secondary association degree analysis result is greater than or equal to a secondary association degree threshold value, adding the processing parameters of the element to be optimized into the memory to-be-optimized processing parameters.
Specifically, the memory process record data is production data during production processing of the memory element, and includes data such as processing time, processing amount, processing equipment parameters, and the like. The association degree analysis function is a function for performing quantization analysis on association relations between independent variables and dependent variables. And carrying out a function of quantitatively analyzing the association degree between the processing parameters of the element to be optimized and the performance indexes to be optimized through the association degree analysis function, and obtaining the association degree through function calculation.
Specifically, according to the memory processing record data, the x variation of the element processing parameter to be optimized and the y variation of the memory processing record data can be obtained respectively. And inputting the data value in the memory processing record data into the association analysis function to perform association analysis, so as to obtain the secondary association analysis result. The secondary association degree threshold value is the lowest association degree value of the association relation between the processing parameters of the element to be optimized and the performance index to be optimized meeting association requirements. And screening the secondary association degree analysis result by using the secondary association degree threshold value, so that the processing parameters of the element to be optimized corresponding to the secondary association degree analysis result meeting the requirements are added into the processing parameters to be optimized of the memory. Therefore, the method and the device achieve the technical effects of determining the processing parameters related to the unqualified performance of the memory and accurately determining the parameters affecting the performance detection result, thereby providing reliable basis for improving the performance of the memory.
Step S500: optimizing design is carried out on the processing parameters to be optimized of the memory according to the performance indexes to be optimized, and a memory optimization scheme is obtained;
further, the optimizing design is performed on the processing parameters to be optimized of the memory according to the performance index to be optimized, and a memory optimizing scheme is obtained, and step S500 of the embodiment of the present application further includes:
step S510: taking the processing parameters to be optimized of the memory as independent variable groups, taking the performance indexes to be optimized as dependent variables, and collecting memory processing particle swarms;
step S520: constructing an optimization fitness function;
step S530: constructing a particle swarm optimization space based on the memory processing particle swarm and the optimization fitness function;
step S540: and outputting the memory optimization scheme based on the particle swarm optimization space iteration prediction times.
Further, the step S520 of the embodiment of the present application further includes:
step S521: acquiring fitness function evaluation indexes, wherein the fitness function evaluation indexes comprise particle selection frequency and index state deviation degree;
step S522: setting a first weight for the particle selection frequency and a second weight for the index state deviation degree;
Step S523: and weighting the particle selection frequency and the index state deviation degree according to the first weight and the second weight, and constructing the optimized fitness function.
Specifically, the performance index to be optimized is taken as an optimization design target, in other words, the performance requirement of the memory can be met in the performance index to be optimized by optimizing and designing the processing parameters to be optimized of the memory. The to-be-optimized performance index can be changed by taking the to-be-optimized processing parameter of the memory as an independent variable group and taking the to-be-optimized performance index as a dependent variable, that is to say, the to-be-optimized processing parameter of the memory is changed. The memory processing particle swarm is a parameter range which is selected by the memory and can be selected by the processing parameters to be optimized during production and processing. The optimization fitness function is used for carrying out quantization calculation on the contribution degree of the processing parameters to be optimized to the index state, and determining the value of the processing parameters to be optimized to obtain the index state.
Specifically, in the process of constructing the optimized fitness function, firstly, the influence degree of the value of the processing parameter on the index state is determined by acquiring the fitness function evaluation index. The particle selection frequency is the number of times the processing parameter in the memory processing particle group is selected in a unit time. The index state deviation degree is the deviation degree between the performance index of the memory and the performance index meeting the requirement by selecting the processing parameters in the memory processing particle swarm.
Specifically, the particle selection frequency is set as a first weight, and the index state deviation degree is set as a second weight, that is, the particle selection frequency and the index state deviation degree are set as two evaluation factors for the influence degree of the processing parameters on the performance index. The weight values of the first weight and the second weight are set by the staff, and are not limited herein. And carrying out weight assignment on the particle selection frequency and the index state deviation according to the first weight and the second weight, and obtaining the optimized fitness function according to an assignment result.
Specifically, the particle swarm optimization space is constructed by processing a particle swarm base through the memory and taking the optimization fitness function as a parameter evaluation function. The particle swarm optimization space is a range for optimally designing the processing parameters to be optimized of the memory. And inputting the processing parameters to be optimized of the memory into the particle swarm optimization space, continuously carrying out optimization iteration on the processing parameters through the processing particle swarm of the memory in the particle swarm optimization space, and evaluating the optimized processing parameters according to the optimization fitness function until the memory optimized processing parameters meeting the requirements are output. And taking the memory optimization processing parameters meeting the requirements as the memory optimization scheme. The intelligent detection of the storage performance of the flash memory is achieved, the output of an optimization scheme is carried out on the detected indexes which do not meet the requirements, and the technical effect of improving the quality of the memory is achieved.
Step S600: adding the performance index to be optimized and the memory optimization scheme into a memory performance detection result;
step S700: and sending the storage performance detection result to a floating gate type memory production management terminal.
Specifically, the performance index to be optimized and the memory optimization scheme are used as detection results after performance detection of the flash memory, namely the storage performance detection results. And sending the storage performance detection result to a floating gate type memory production management terminal, so as to provide reliable management basis for production quality management of the memory.
In summary, the embodiment of the application has at least the following technical effects:
according to the application, performance detection indexes of the memory and the range, which corresponds to each index and accords with the standard, are obtained, the flash memory is subjected to item-by-item index detection, a corresponding detection index state value matrix is obtained according to detection results, index screening is carried out on index state values, the performance detection indexes which do not meet index thresholds are set as performance indexes to be optimized, primary association analysis is carried out on the association degree between the performance indexes to be optimized and memory elements, secondary association analysis is carried out on the processing parameters of the elements to be optimized according to the performance indexes to be optimized, so that the processing parameters to be optimized of the memory are obtained, the processing parameters to be optimized of the memory are optimally designed according to the performance indexes to be optimized, a specific scheme for optimizing the processing parameters of the memory is obtained, the storage performance of the flash memory is intelligently detected by sending the storage performance detection results to a management terminal, reliable basis is provided for production management of the memory according to the detection results, and the production quality of the memory is improved.
Examples
Based on the same inventive concept as the method for detecting the storage performance of a flash memory in the foregoing embodiments, as shown in fig. 4, the present application provides a system for detecting the storage performance of a flash memory, and the system and method embodiments in the embodiments of the present application are based on the same inventive concept. Wherein the system comprises:
a detection index obtaining module 11, where the detection index obtaining module 11 is configured to obtain a storage performance detection index and a storage performance index threshold;
the state value matrix obtaining module 12 is configured to traverse the storage performance detection index to detect the flash memory, and obtain a state value matrix of the detection index;
a performance optimization index setting module 13, where the performance optimization setting module 13 is configured to extract a performance detection index that the detection index state value matrix does not meet the stored performance index threshold, and set the performance detection index as a performance index to be optimized;
the processing parameter obtaining module 14 is configured to perform association analysis according to the performance index to be optimized to obtain a processing parameter to be optimized of a memory;
the optimization scheme obtaining module 15 is used for carrying out optimization design on the processing parameters to be optimized of the memory according to the performance index to be optimized, and obtaining a memory optimization scheme;
The detection result obtaining module 16, where the detection result obtaining module 16 is configured to add the performance index to be optimized and the memory optimization scheme into a storage performance detection result;
and a result transmitting module 17, wherein the result transmitting module 17 is used for transmitting the storage performance detection result to the floating gate type memory production management terminal.
Further, the system further comprises:
an optoelectronic performance index setting unit configured to set the storage performance detection index to include an electrical storage performance detection index including one or more of a current-voltage curve, an electrical storage transfer characteristic curve, a charge retention performance, and a service life, and an optoelectronic storage performance detection index including one or more of an electrical storage transfer characteristic curve, FN tunneling mechanism performance, light response performance, light-assisted erasure performance, and optoelectronic multi-value storage performance;
a storage index threshold setting unit for traversing one or more of the current-voltage curve, the electrical storage transfer characteristic curve, the charge retention property, and the service life, and setting the storage performance index threshold for one or more of the electrical storage transfer characteristic curve, the FN tunneling mechanism property, the photoresponsive property, the optically assisted erasure property, and the photoelectric multi-value storage property.
Further, the system further comprises:
the deviation interval setting unit is used for traversing the storage performance index threshold value and setting a fault-tolerant deviation interval;
the detection matrix construction unit is used for constructing an index state detection matrix according to the storage performance index threshold value and the fault tolerance deviation interval;
and the to-be-optimized index setting unit is used for setting elements which do not meet the index state detection matrix in the detection index state value matrix as the to-be-optimized performance index.
Further, the system further comprises:
the element to be optimized obtaining unit is used for carrying out primary association analysis according to the performance index to be optimized to obtain the element to be optimized of the memory;
the processing parameter obtaining unit is used for obtaining the processing parameters of the element to be optimized according to the element to be optimized of the memory;
and the processing parameter obtaining unit to be optimized is used for carrying out secondary association analysis on the processing parameters of the element to be optimized according to the performance index to be optimized to obtain the processing parameters to be optimized of the memory.
Further, the system further comprises:
the processing record data acquisition unit is used for acquiring processing record data of a memory by taking any one of the processing parameters of the element to be optimized as an independent variable and the performance index to be optimized as a dependent variable;
an analysis function obtaining unit configured to obtain a relevance analysis function:
wherein,characterizing a first association coefficient, ">Characterizing a second association coefficient, ">Characterizing the association degree->Characterizing the processing parameters of any element to be optimized, < ->Characterizing performance index to be optimized, < >>Characterizing the i-th pair of memory process record data>Variable amount of->Characterizing the i-th pair of memory process record data>Variable amount of->Characterizing the total logarithm of memory processing record data;
the association degree evaluation unit is used for performing association degree evaluation based on the association degree analysis function according to the memory processing record data to obtain a secondary association degree analysis result;
and the processing parameter adding unit is used for adding the processing parameters of the element to be optimized into the processing parameters to be optimized of the memory when the secondary association degree analysis result is larger than or equal to a secondary association degree threshold value.
Further, the system further comprises:
the particle swarm acquisition unit is used for acquiring a memory processing particle swarm by taking the memory processing parameter to be optimized as an independent variable group and the performance index to be optimized as a dependent variable;
the fitness function construction unit is used for constructing an optimized fitness function;
an optimized space construction unit, which is used for constructing a particle swarm optimized space based on the memory processing particle swarm and the optimized fitness function;
and the optimization scheme output unit is used for outputting the memory optimization scheme based on the particle swarm optimization space iteration prediction times.
Further, the system further comprises:
the evaluation index acquisition unit is used for acquiring fitness function evaluation indexes, wherein the fitness function evaluation indexes comprise particle selection frequency and index state deviation degree;
the second weight setting unit is used for setting a first weight for the particle selection frequency and a second weight for the index state deviation degree;
And the deviation degree weighting unit is used for weighting the particle selection frequency and the index state deviation degree according to the first weight and the second weight, and constructing the optimization fitness function.
It should be noted that the sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. And the foregoing description has been directed to specific embodiments of this specification. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
The foregoing description of the preferred embodiments of the application is not intended to limit the application to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the application are intended to be included within the scope of the application.
The specification and figures are merely exemplary illustrations of the present application and are considered to cover any and all modifications, variations, combinations, or equivalents that fall within the scope of the application. It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the scope of the application. Thus, the present application is intended to include such modifications and alterations insofar as they come within the scope of the application or the equivalents thereof.
Claims (6)
1. The method is characterized by being applied to a floating gate type memory detection module and comprising the following steps of:
acquiring a storage performance detection index and a storage performance index threshold;
detecting the flash memory by traversing the storage performance detection indexes to obtain a detection index state value matrix;
extracting performance detection indexes of which the detection index state value matrix does not meet the storage performance index threshold value, and setting the performance detection indexes as performance indexes to be optimized;
performing association analysis according to the performance index to be optimized to obtain processing parameters to be optimized of a memory;
optimizing design is carried out on the processing parameters to be optimized of the memory according to the performance indexes to be optimized, and a memory optimization scheme is obtained;
Adding the performance index to be optimized and the memory optimization scheme into a memory performance detection result;
transmitting the storage performance detection result to a floating gate type memory production management terminal;
the optimizing design is performed on the processing parameters to be optimized of the memory according to the performance index to be optimized, and a memory optimizing scheme is obtained, including:
taking the processing parameters to be optimized of the memory as independent variable groups, taking the performance indexes to be optimized as dependent variables, and collecting memory processing particle swarms;
constructing an optimization fitness function;
constructing a particle swarm optimization space based on the memory processing particle swarm and the optimization fitness function;
outputting the memory optimization scheme based on the particle swarm optimization space iteration prediction times;
the construction of the optimized fitness function comprises the following steps:
acquiring fitness function evaluation indexes, wherein the fitness function evaluation indexes comprise particle selection frequency and index state deviation degree;
setting a first weight for the particle selection frequency and a second weight for the index state deviation degree;
and weighting the particle selection frequency and the index state deviation degree according to the first weight and the second weight, and constructing the optimized fitness function.
2. The method for detecting storage performance of a flash memory according to claim 1, wherein obtaining a storage performance detection index and a storage performance index threshold value comprises:
the storage performance detection indicators comprise an electrical storage performance detection indicator and an optoelectronic storage performance detection indicator, wherein the electrical storage performance detection indicator comprises one or more of a current-voltage curve, an electrical storage transfer characteristic curve, a charge retention performance and a service life, and the optoelectronic storage performance detection indicator comprises one or more of an optoelectronic storage transfer characteristic curve, FN tunneling mechanism performance, photo-response performance, photo-assisted erasure performance and optoelectronic multi-value storage performance;
the storage performance index threshold is set by traversing one or more of the current-voltage curve, the electrical storage transfer characteristic, the charge retention performance, and the service life, and one or more of the optoelectronic storage transfer characteristic, the FN tunneling mechanism performance, the photoresponsive performance, the optically assisted erasure performance, and the optoelectronic multi-valued storage performance.
3. The method for detecting storage performance of a flash memory according to claim 1, wherein extracting a performance detection index whose detection index state value matrix does not satisfy the storage performance index threshold value, to be set as a performance index to be optimized, comprises:
Traversing the storage performance index threshold value, and setting a fault tolerance deviation interval;
constructing an index state detection matrix according to the storage performance index threshold value and the fault tolerance deviation interval;
and setting elements which do not meet the index state detection matrix in the detection index state value matrix as the performance indexes to be optimized.
4. The method for detecting storage performance of a flash memory according to claim 1, wherein performing association analysis according to the performance index to be optimized to obtain processing parameters to be optimized of the flash memory comprises:
performing primary association analysis according to the performance index to be optimized to obtain a memory element to be optimized;
acquiring processing parameters of the element to be optimized according to the element to be optimized of the memory;
and carrying out secondary association analysis on the processing parameters of the element to be optimized according to the performance index to be optimized, and obtaining the processing parameters to be optimized of the memory.
5. The method for detecting storage performance of a flash memory according to claim 4, wherein performing a two-level association analysis on the processing parameters of the element to be optimized according to the performance index to be optimized to obtain the processing parameters to be optimized of the flash memory comprises:
Taking any one of the processing parameters of the element to be optimized as an independent variable, taking the performance index to be optimized as a dependent variable, and collecting processing record data of a memory;
acquiring a relevance analysis function:
，
，
，
wherein,characterizing a first association coefficient, ">Characterizing a second association coefficient, ">The degree of association is characterized in that,characterizing the processing parameters of any element to be optimized, < ->Characterizing performance index to be optimized, < >>Characterizing the i-th pair of memory process record data>Variable amount of->Characterizing the i-th pair of memory process record data>Variable amount of->Characterizing the total logarithm of memory processing record data;
performing relevance evaluation based on the relevance analysis function according to the memory processing record data to obtain a secondary relevance analysis result;
and when the secondary association degree analysis result is greater than or equal to a secondary association degree threshold value, adding the processing parameters of the element to be optimized into the memory to-be-optimized processing parameters.
6. A storage performance detection system for a flash memory, comprising:
the detection index obtaining module is used for obtaining a storage performance detection index and a storage performance index threshold;
the state value matrix obtaining module is used for traversing the storage performance detection indexes to detect the flash memory and obtaining a detection index state value matrix;
The performance optimization index setting module is used for extracting performance detection indexes of which the detection index state value matrix does not meet the storage performance index threshold value and setting the performance detection indexes as performance indexes to be optimized;
the processing parameter obtaining module is used for carrying out association analysis according to the performance index to be optimized to obtain the processing parameter to be optimized of the memory;
the optimization scheme obtaining module is used for carrying out optimization design on the processing parameters to be optimized of the memory according to the performance indexes to be optimized to obtain a memory optimization scheme;
the detection result obtaining module is used for adding the performance index to be optimized and the memory optimization scheme into a storage performance detection result;
the result sending module is used for sending the storage performance detection result to a floating gate type memory production management terminal;
the optimization scheme obtaining module further comprises:
the particle swarm acquisition unit is used for acquiring a memory processing particle swarm by taking the memory processing parameter to be optimized as an independent variable group and the performance index to be optimized as a dependent variable;
The fitness function construction unit is used for constructing an optimized fitness function;
an optimized space construction unit, which is used for constructing a particle swarm optimized space based on the memory processing particle swarm and the optimized fitness function;
the optimization scheme output unit is used for outputting the memory optimization scheme based on the particle swarm optimization space iteration prediction times;
the evaluation index acquisition unit is used for acquiring fitness function evaluation indexes, wherein the fitness function evaluation indexes comprise particle selection frequency and index state deviation degree;
the second weight setting unit is used for setting a first weight for the particle selection frequency and a second weight for the index state deviation degree;
and the deviation degree weighting unit is used for weighting the particle selection frequency and the index state deviation degree according to the first weight and the second weight, and constructing the optimization fitness function.
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